splunk-otel-util-genai-evals 0.1.7

OpenTelemetry GenAI Evaluations

This package provides the base evaluation manager and builtin evaluators for opentelemetry-util-genai. It is loaded dynamically by the core GenAI telemetry utilities via the completion callback plugin mechanism.

Features

• Evaluation Manager - Background processing of LLM and agent evaluations

• Concurrent Processing - Multi-worker parallel evaluation for high throughput

• Bounded Queue - Backpressure support to prevent memory exhaustion

• Async Evaluation - Native async support for LLM-as-a-Judge evaluators

Concurrent Evaluation Mode

Enable concurrent processing for improved throughput with LLM-based evaluations:

# Enable concurrent mode with 4 workers
export OTEL_INSTRUMENTATION_GENAI_EVALS_CONCURRENT=true
export OTEL_INSTRUMENTATION_GENAI_EVALS_WORKERS=4

# Optional: Bounded queue for backpressure
export OTEL_INSTRUMENTATION_GENAI_EVALS_QUEUE_SIZE=100

Sequential Mode (Default):

• Single worker thread processes evaluations one at a time

• Guaranteed ordering of evaluation results

• Lower resource consumption

Concurrent Mode:

• Multiple worker threads with asyncio event loops

• Parallel LLM API calls for faster evaluation throughput

• Recommended for LLM-as-a-Judge evaluators (e.g., DeepEval)

Creating Custom Evaluators

Implement the Evaluator base class and optionally override async methods for native async support:

from opentelemetry.util.genai.evals.base import Evaluator, EvaluationResult

class MyEvaluator(Evaluator):
    @property
    def supports_async(self) -> bool:
        return True  # Enable native async evaluation

    async def evaluate_llm_async(self, invocation):
        # Your async evaluation logic
        return [EvaluationResult(name="my_metric", score=0.95)]